Hourly Long-Term Traffic Volume Prediction with Meteorological Information Using Graph Convolutional Networks
Author:
Affiliation:
1. Lyles School of Civil Engineering, Purdue University, West Lafayette, IN 47907, USA
2. Department of Transportation Planning & Management, Korea National University of Transportation, Chungju 27469, Republic of Korea
Abstract
Funder
Ministry of Land, Infrastructure and Transport
Publisher
MDPI AG
Link
https://www.mdpi.com/2076-3417/14/6/2285/pdf
Reference23 articles.
1. Shang, Q., Lin, C., Yang, Z., Bing, Q., and Zhou, X. (2016). A hybrid short-term traffic flow prediction model based on singular spectrum analysis and kernel extreme learning machine. PLoS ONE, 11.
2. Short-term traffic demand prediction using graph convolutional neural networks;Li;AGILE GIScience Ser.,2020
3. Wang, Y., Zhao, L., Li, S., Wen, X., and Xiong, Y. (2020). Short-term traffic flow prediction of the urban road using time-varying filtering based empirical mode decomposition. Appl. Sci., 10.
4. Traffic prediction using artificial intelligence: Review of recent advances and emerging opportunities;Shaygan;Transp. Res. Part Emerg. Technol.,2022
5. Song, Z., Guo, Y., Wu, Y., and Ma, J. (2019). Short-term traffic speed prediction under different data collection time intervals using a SARIMA-SDGM hybrid prediction model. PLoS ONE, 6.
1.学者识别学者识别
2.学术分析学术分析
3.人才评估人才评估
"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370
www.globalauthorid.com
TOP
Copyright © 2019-2024 北京同舟云网络信息技术有限公司 京公网安备11010802033243号 京ICP备18003416号-3